International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | The Convergence of Cognition and Information: a Holistic Framework for Machine Intelligence and Smart Data Technologies |
|---|---|
| ABSTRACT | The evolution of artificial intelligence has reached an inflection point where raw computational power and isolated algorithmic advances are insufficient to address the complexity of real-world problems. This paper introduces and explores the synergistic paradigm of Machine Intelligence (MI)—encompassing not just machine learning, but also reasoning, adaptation, and autonomous decision-making—powered by Smart Data Technologies (SDT)—systems that actively curate, enhance, and govern data throughout its lifecycle. We posit that the next leap in capability will arise from their deep integration, creating systems where intelligence is embedded within a self-optimizing data ecosystem. A comprehensive literature survey traces the lineage from data mining and business intelligence to contemporary deep learning and data-centric AI, establishing smart data as the essential substrate for robust machine intelligence. We propose the Intelligent Data Fabric (IDF), a novel conceptual architecture that unifies autonomous data pipelines, self-describing metadata (data intelligence), and adaptive machine intelligence models into a single, cognitive data plane. To validate this framework, a mixed-methodology approach was employed. First, a controlled simulation of a smart city traffic management system was developed, comparing a traditional data warehouse + ML model approach against an IDF-powered system. Second, a real-world case study of a financial fraud detection platform transitioning to an MI-SDT architecture was analyzed. The simulation results demonstrated that the IDF system achieved a 42% faster anomaly detection response time and a 35% reduction in false positives by dynamically retraining models on freshly curated, context-rich data streams. The financial case study revealed a 60% decrease in time-to-insight for new fraud patterns and a 50% reduction in data preparation overhead. Crucially, the study identifies metacognition—the system's ability to monitor its own data quality, model performance, and knowledge gaps—as the critical emergent property of this integration. The analysis concludes that the future of enterprise and societal-scale AI lies in moving from 'big data' to 'intelligent data,' where data is not passively stored but actively participates in the learning and reasoning loop. Key research frontiers include neuro-symbolic integration within data fabrics, federated intelligence across distributed smart data silos, and the development of ethical frameworks for autonomous data curation. The MI-SDT convergence represents a foundational shift towards truly autonomous, resilient, and context-aware intelligent systems. |
| AUTHOR | S. SANGEETHA Assistant Professor, Dept. of Artificial Intelligence and Data Science, Jerusalem College of Engineering, Pallikaranai, Chennai, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312092 |
| pdf/92_The Convergence of Cognition and Information a Holistic Framework for Machine Intelligence and Smart Data Technologies.pdf | |
| KEYWORDS | |
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